Activity Based Intelligence and Street-level Feature Data
I had the opportunity to show off some of our data to current and potential government and industry customers using Fulcrum at the 2015 GEOINT Symposium. Our data products provide fine scale point of interest (POI) and cadastral data for almost any area in the world. As I spoke with government customers, the concept of foundational data came up often. With the popularity of activity-based intelligence (ABI) in the GEOINT community this year I wanted to place our data products within this framework. In this post, I suggest that within an ABI context, high-fidelity, fine scale POI and cadastral data is the most important foundational layer.
When I show off our data I stress the importance of the rich attributes associated with our features. Beyond the typical name, feature type, and location of a POI our features typically have over 30 associated administrative and sociodemographic attributes, many of which represent persistent space-time objects within an ABI context. Attributes such as phone numbers, owners, and websites associated with a parcel boundary, building footprint, or fine scale geohash act as critical anchors when conducting high-performance vector conflation using tools like the NGA’s hootenanny.
Within Hägerstrand’s time-geographic framework, the relative space-time stability and confidence of high-fidelity feature data serves as the foundation for analyses within the space-time prism (Figure 1). The space-time dynamism of movement tracks, geotagged social media, and other volatile data typically used in this context requires a stable foundation such as accurate POI or cadastral data in which to build relationships from. This difference in stability and confidence between high-fidelity POI data and dynamic space-time features is particularly apparent within urban areas where the amount of objects and activities are exceptionally dense. The temporal stability of in situ verified POI data also transcends ABI contexts to operational and tactical planning situations. I had an opportunity to see some of our data being used in this capacity within our partner Pixia’s multi-intelligence search and analysis application InSITE. Using its underlying search capability Swoop, users can search and analyze large amounts of disparate data from multiple sources including georeferenced video, imagery, and feature data. One of the most impressive features of InSITE is its ability to seamlessly switch analytical and planning contexts. As I watched a demonstration of the application the commonality when switching contexts was street-level feature data. Our data was involved in searching for entities and objects using keywords, determining associations between businesses and movement tracks, and high resolution 3D physical environment planning.
As ABI matures, I believe the spatial resolution of street-level feature data coupled with the temporal persistence of rich attribution in a high-fidelity POI or cadastral dataset will form the foundational layer for ABI analysis. Attributes and objects associated with street-level feature data can persist for months, years, or even decades and can provide the basis for vector conflation and space-time relationships within an ABI context.